In this article, we examine the performance of two newly developed procedures that jointly select the number of states and variables in Markov-switching models by means of Monte Carlo simulations. They are Smith et al. ( 2006 Smith , A. , Naik , P. A. , Tsai , C. ( 2006 ). Markov-switching model selection using Kullback–Leibler divergence . Journal of Econometrics 134 ( 2 ): 553 – 577 . [Crossref], [Web of Science ®] [Google Scholar] ) and Psaradakis and Spagnolo ( 2006 Psaradakis , Z. , Spagnolo , N. ( 2006 ). Joint determination of the state dimension and autoregressive order for models with Markov regime switching . Journal of Time Series Analysis 27 ( 2 ): 753 – 766 . [Crossref], [Web of Science ®] [Google Scholar] ), respectively. The former develops Markov switching criterion (MSC) designed specifically for Markov-switching models, while the latter recommends the use of standard complexity-penalised information criteria (BIC, HQC, and AIC) in joint determination of the state dimension and the autoregressive order of Markov-switching models. The Monte Carlo evidence shows that BIC outperforms MSC while MSC and HQC are preferable over AIC.

Through empirical analysis, this paper uses panel data from 30 provinces in China (excluding Hong Kong, Macao, and Taiwan, as well as Tibet) from 2010 to 2022 to explore in-depth and empirically test the relationship between the impact of digitalization technology on tourism economic growth and the intrinsic mechanism of its action. The results found that: first, digital technology has a significant impact on tourism economic growth, and second, digital technology has a significant impact on tourism economic growth. Significant promotional utility and, secondly, tourism industry efficiency, play a partial mediating role in the relationship between digital technology and tourism economic growth, i.e., digital technology can promote tourism economic growth by positively affecting tourism industry efficiency. This paper also reveals the role of digital technology in promoting the efficient development of the tourism industry, including the optimal allocation of resources and the improvement of service efficiency, which in turn promotes the innovative development of the tourism economy. Finally, in response to the challenges posed by digital transformation, suggestions are made to strengthen regulation and standardize the market order to ensure the sustainable and high-quality development of the tourism industry.

Background and Aim: This study investigates the impact of internal control on the growth of equipment manufacturing enterprises in China. With the increasing emphasis on corporate governance and risk management, understanding how internal control influences enterprise development is essential. The research draws on the COSO framework of internal control, which outlines its components and emphasizes its role in enhancing operational efficiency and strategic alignment. This theoretical lens provides a foundation for examining whether enhanced internal control mechanisms contribute to improved corporate growth, particularly regarding differences between state-owned and non-state-owned enterprises. Ownership structure, including governance mechanisms and incentive alignment, may influence how internal control practices are implemented, thereby affecting their impact on growth. Materials and Methods: The study uses data from A-share listed equipment manufacturing enterprises in China from 2018 to 2022. Regression analysis, including multiple regression models with control variables, and comparative tests are employed to assess the relationship between internal control quality and enterprise growth. The analysis also explores the moderating role of property rights, distinguishing between state-owned and non-state-owned enterprises. Result: The findings reveal a significant positive correlation between internal control and enterprise growth. Strengthening internal control enhances core competitiveness, reduces operational, financial, and managerial risks, and fosters sustainable development. Further analysis indicates that property rights influence this relationship, with non-state-owned enterprises showing a more pronounced effect. This difference can be attributed to variations in governance structures, incentive mechanisms, and resource allocation between ownership types, which affect the implementation and effectiveness of internal control practices. Conclusion: Internal control plays a crucial role in promoting enterprise growth, especially in non-state-owned enterprises. Policymakers and corporate managers should prioritize improving internal control mechanisms to enhance competitiveness and reduce risks. This study contributes to existing internal control and corporate governance models by extending our understanding of how internal control influences enterprise performance in different ownership contexts. Future research could explore industry-specific variations and longitudinal effects to deepen our understanding of internal control’s impact on enterprise growth.